Abstract: This paper presents a weighted approach to unconstrained iris recognition. In nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition.
Abstract: In this paper, we propose a robust scheme to work face alignment and recognition under various influences. For face representation, illumination influence and variable expressions are the important factors, especially the accuracy of facial localization and face recognition. In order to solve those of factors, we propose a robust approach to overcome these problems. This approach consists of two phases. One phase is preprocessed for face images by means of the proposed illumination normalization method. The location of facial features can fit more efficient and fast based on the proposed image blending. On the other hand, based on template matching, we further improve the active shape models (called as IASM) to locate the face shape more precise which can gain the recognized rate in the next phase. The other phase is to process feature extraction by using principal component analysis and face recognition by using support vector machine classifiers. The results show that this proposed method can obtain good facial localization and face recognition with varied illumination and local distortion.
Abstract: Robust face recognition under various illumination
environments is very difficult and needs to be accomplished for
successful commercialization. In this paper, we propose an improved
illumination normalization method for face recognition. Illumination
normalization algorithm based on anisotropic smoothing is well known
to be effective among illumination normalization methods but
deteriorates the intensity contrast of the original image, and incurs less
sharp edges. The proposed method in this paper improves the previous
anisotropic smoothing-based illumination normalization method so
that it increases the intensity contrast and enhances the edges while
diminishing the effect of illumination variations. Due to the result of
these improvements, face images preprocessed by the proposed
illumination normalization method becomes to have more distinctive
feature vectors (Gabor feature vectors) for face recognition. Through
experiments of face recognition based on Gabor feature vector
similarity, the effectiveness of the proposed illumination
normalization method is verified.